OpenRaiser/Intern-Atlas huggingface dataset:
Pushing the Envelope of Rotation Averaging for Visual SLAM
Venue: arXiv cs.CV 2020
Year: 2020

As an essential part of structure from motion (SfM) and Simultaneous Localization and Mapping (SLAM) systems, motion averaging has been extensively studied in the past years and continues to attract surging research attention. While canonical approaches such as bundle adjustment are predominantly inherited in most of state-of-the-art SLAM systems to estimate and update the trajectory in the robot navigation, the practical implementation of bundle adjustment in SLAM systems is intrinsically limited by the high computational complexity, unreliable convergence and strict requirements of ideal initializations. In this paper, we lift these limitations and propose a novel optimization backbone for visual SLAM systems, where we leverage rotation averaging to improve the accuracy, efficiency and robustness of conventional monocular SLAM pipelines. In our approach, we first decouple the rotational and translational parameters in the camera rigid body transformation and convert the high-dimensional non-convex nonlinear problem into tractable linear subproblems in lower dimensions, and show that the subproblems can be solved independently with proper constraints. We apply the scale parameter with $l_1$-norm in the pose-graph optimization to address the rotation averaging robustness against outliers. We further validate the global optimality of our proposed approach, revisit and address the initialization schemes, pure rotational scene handling and outlier treatments. We demonstrate that our approach can exhibit up to 10x faster speed with comparable accuracy against the state of the art on public benchmarks.
Bilevel Continual Learning
Venue: arXiv cs.LG 2020
Year: 2020

Continual learning (CL) studies the problem of learning a sequence of tasks, one at a time, such that the learning of each new task does not lead to the deterioration in performance on the previously seen ones while exploiting previously learned features. This paper presents Bilevel Continual Learning (BiCL), a general framework for continual learning that fuses bilevel optimization and recent advances in meta-learning for deep neural networks. BiCL is able to train both deep discriminative and generative models under the conservative setting of the online continual learning. Experimental results show that BiCL provides competitive performance in terms of accuracy for the current task while reducing the effect of catastrophic forgetting. This is a concurrent work with [1]. We submitted it to AAAI 2020 and IJCAI 2020. Now we put it on the arxiv for record. Different from [1], we also consider continual generative model as well. At the same time, the authors are aware of a recent proposal on bilevel optimization based coreset construction for continual learning [2]. [1] Q. Pham, D. Sahoo, C. Liu, and S. C. Hoi. Bilevel continual learning. arXiv preprint arXiv:2007.15553, 2020. [2] Z. Borsos, M. Mutny, and A. Krause. Coresets via bilevel optimization for continual learning and streaming. arXiv preprint arXiv:2006.03875, 2020
Reducing Neural Network Parameter Initialization Into an SMT Problem
Venue: arXiv cs.LG 2020
Year: 2020

Training a neural network (NN) depends on multiple factors, including but not limited to the initial weights. In this paper, we focus on initializing deep NN parameters such that it performs better, comparing to random or zero initialization. We do this by reducing the process of initialization into an SMT solver. Previous works consider certain activation functions on small NNs, however the studied NN is a deep network with different activation functions. Our experiments show that the proposed approach for parameter initialization achieves better performance comparing to randomly initialized networks.
There's No Trick, Its Just a Simple Trick: A Web-Compat and Privacy Improving Approach to Third-party Web Storage
Venue: arXiv cs.CR 2020
Year: 2020

While much current web privacy research focuses on browser fingerprinting, the boring fact is that the majority of current third-party web tracking is conducted using traditional, persistent-state identifiers. One possible explanation for the privacy community's focus on fingerprinting is that to date browsers have faced a lose-lose dilemma when dealing with third-party stateful identifiers: block state in third-party frames and break a significant number of webpages, or allow state in third-party frames and enable pervasive tracking. The alternative, middle-ground solutions that have been deployed all trade privacy for compatibility, rely on manually curated lists, or depend on the user to manage state and state-access themselves. This work furthers privacy on the web by presenting a novel system for managing the lifetime of third-party storage, "page-length storage". We compare page-length storage to existing approaches for managing third-party state and find that page-length storage has the privacy protections of the most restrictive current option (i.e., blocking third-party storage) but web-compatibility properties mostly similar to the least restrictive option (i.e., allowing all third-party storage). This work further compares page-length storage to an alternative third-party storage partitioning scheme and finds that page-length storage provides superior privacy protections with comparable web-compatibility. We provide a dataset of the privacy and compatibility behaviors observed when applying the compared third-party storage strategies on a crawl of the Tranco 1k and the quantitative metrics used to demonstrate that page-length storage matches or surpasses existing approaches. Finally, we provide an open-source implementation of our page-length storage approach, implemented as patches against Chromium.
Impact of Community Structure on Consensus Machine Learning
Venue: arXiv cs.LG 2020
Year: 2020

Consensus dynamics support decentralized machine learning for data that is distributed across a cloud compute cluster or across the internet of things. In these and other settings, one seeks to minimize the time $τ_ε$ required to obtain consensus within some $ε>0$ margin of error. $τ_ε$ typically depends on the topology of the underlying communication network, and for many algorithms $τ_ε$ depends on the second-smallest eigenvalue $λ_2\in[0,1]$ of the network's normalized Laplacian matrix: $τ_ε\sim\mathcal{O}(λ_2^{-1})$. Here, we analyze the effect on $τ_ε$ of network community structure, which can arise when compute nodes/sensors are spatially clustered, for example. We study consensus machine learning over networks drawn from stochastic block models, which yield random networks that can contain heterogeneous communities with different sizes and densities. Using random matrix theory, we analyze the effects of communities on $λ_2$ and consensus, finding that $λ_2$ generally increases (i.e., $τ_ε$ decreases) as one decreases the extent of community structure. We further observe that there exists a critical level of community structure at which $τ_ε$ reaches a lower bound and is no longer limited by the presence of communities. We support our findings with empirical experiments for decentralized support vector machines.
Essential Scattering Applications for Everyone. Overview
Venue: arXiv cs.CE 2020
Year: 2020

ESCAPE is a free python package and framework for creating applications for simulating and fitting of X-ray and neutron scattering data with current support for specular reflectivity, polarized neutron reflectometry, high resolution X-ray diffraction, small angle scattering with future support for off-specular scattering from structured samples with complicated morphology. Utilizing current features of Jupyter project, it allows to create highly customized applications in the format of notebooks. These notebooks, being shared with other users, can be used directly or started as web applications with graphical user interface. This paper is a brief overview of the core and scattering packages providing description of the major features with code examples. The following features make ESCAPE different from other projects: independent from scattering applications core, which provides access to models building blocks like parameters, variables, functors, data objects, models and optimizers; support of arithmetic operations and algebraic expressions on parameters and functors, offering models with complex dependencies of parameters; math module with standard mathematical functors and special functors which perform numerical integration over variable or parameter, supplying customization of intensity model; simultaneous fit of several models, also for models with different dimensions. Check our web site https://escape-app.net/ for further information.
Distributed Machine Learning for Computational Engineering using MPI
Venue: arXiv cs.DC 2020
Year: 2020

We propose a framework for training neural networks that are coupled with partial differential equations (PDEs) in a parallel computing environment. Unlike most distributed computing frameworks for deep neural networks, our focus is to parallelize both numerical solvers and deep neural networks in forward and adjoint computations. Our parallel computing model views data communication as a node in the computational graph for numerical simulations. The advantage of our model is that data communication and computing are cleanly separated and thus provide better flexibility, modularity, and testability. We demonstrate using various large-scale problems that we can achieve substantial acceleration by using parallel solvers for PDEs in training deep neural networks that are coupled with PDEs.
On the Relevance-Complexity Region of Scalable Information Bottleneck
Venue: arXiv cs.IT 2020
Year: 2020

The Information Bottleneck method is a learning technique that seeks a right balance between accuracy and generalization capability through a suitable tradeoff between compression complexity, measured by minimum description length, and distortion evaluated under logarithmic loss measure. In this paper, we study a variation of the problem, called scalable information bottleneck, where the encoder outputs multiple descriptions of the observation with increasingly richer features. The problem at hand is motivated by some application scenarios that require varying levels of accuracy depending on the allowed level of generalization. First, we establish explicit (analytic) characterizations of the relevance-complexity region for memoryless Gaussian sources and memoryless binary sources. Then, we derive a Blahut-Arimoto type algorithm that allows us to compute (an approximation of) the region for general discrete sources. Finally, an application example in the pattern classification problem is provided along with numerical results.
Exempla Gratis (E.G.): Code Examples for Free
Venue: arXiv cs.SE 2020
Year: 2020

Modern software engineering often involves using many existing APIs, both open source and, in industrial coding environments, proprietary. Programmers reference documentation and code search tools to remind themselves of proper common usage patterns of APIs. However, high-quality API usage examples are computationally expensive to curate and maintain, and API usage examples retrieved from company-wide code search can be tedious to review. We present a tool, EG, that mines codebases and shows the common, idiomatic usage examples for API methods. EG was integrated into Facebook's internal code search tool for the Hack language and evaluated on open-source GitHub projects written in Python. EG was also compared against code search results and hand-written examples from a popular programming website called ProgramCreek. Compared with these two baselines, examples generated by EG are more succinct and representative with less extraneous statements. In addition, a survey with Facebook developers shows that EG examples are preferred in 97 percent of cases.
Self-semi-supervised Learning to Learn from NoisyLabeled Data
Venue: arXiv cs.LG 2020
Year: 2020

The remarkable success of today's deep neural networks highly depends on a massive number of correctly labeled data. However, it is rather costly to obtain high-quality human-labeled data, leading to the active research area of training models robust to noisy labels. To achieve this goal, on the one hand, many papers have been dedicated to differentiating noisy labels from clean ones to increase the generalization of DNN. On the other hand, the increasingly prevalent methods of self-semi-supervised learning have been proven to benefit the tasks when labels are incomplete. By 'semi' we regard the wrongly labeled data detected as un-labeled data; by 'self' we choose a self-supervised technique to conduct semi-supervised learning. In this project, we designed methods to more accurately differentiate clean and noisy labels and borrowed the wisdom of self-semi-supervised learning to train noisy labeled data.
Developing High Quality Training Samples for Deep Learning Based Local Climate Zone Classification in Korea
Venue: arXiv cs.CV 2020
Year: 2020

Two out of three people will be living in urban areas by 2050, as projected by the United Nations, emphasizing the need for sustainable urban development and monitoring. Common urban footprint data provide high-resolution city extents but lack essential information on the distribution, pattern, and characteristics. The Local Climate Zone (LCZ) offers an efficient and standardized framework that can delineate the internal structure and characteristics of urban areas. Global-scale LCZ mapping has been explored, but are limited by low accuracy, variable labeling quality, or domain adaptation challenges. Instead, this study developed a custom LCZ data to map key Korean cities using a multi-scale convolutional neural network. Results demonstrated that using a novel, custom LCZ data with deep learning can generate more accurate LCZ map results compared to conventional community-based LCZ mapping with machine learning as well as transfer learning of the global So2Sat dataset.
Balanced Partitioning of Several Cache-Oblivious Algorithms
Venue: arXiv cs.DC 2020
Year: 2020

Frigo et al. proposed an ideal cache model and a recursive technique to design sequential cache-efficient algorithms in a cache-oblivious fashion. Ballard et al. pointed out that it is a fundamental open problem to extend the technique to an arbitrary architecture. Ballard et al. raised another open question on how to parallelize Strassen's algorithm exactly and efficiently on an arbitrary number of processors. We propose a novel way of partitioning a cache-oblivious algorithm to achieve perfect strong scaling on an arbitrary number, even a prime number, of processors within a certain range in a shared-memory setting. Our approach is Processor-Aware but Cache-Oblivious (PACO). We demonstrate our approach on several important cache-oblivious algorithms, including LCS, 1D, GAP, classic rectangular matrix multiplication on a semiring, and Strassen's algorithm. We discuss how to extend our approach to a distributed-memory architecture, or even a heterogeneous computing system. Hence, our work may provide a new perspective on the fundamental open problem of extending the recursive cache-oblivious technique to an arbitrary architecture. We provide an almost exact solution to the open problem on parallelizing Strassen. Our approach may provide a new perspective on extending the recursive cache-oblivious technique to an arbitrary architecture. All our algorithms demonstrate better scalability or better overall parallel cache complexities than the best known algorithms. Preliminary experiments justify our theoretical prediction that the PACO algorithms can outperform significantly state-of-the-art Processor-Oblivious (PO) and Processor-Aware (PA) counterparts.
Participation in TREC 2020 COVID Track Using Continuous Active Learning
Venue: arXiv cs.IR 2020
Year: 2020

We describe our participation in all five rounds of the TREC 2020 COVID Track (TREC-COVID). The goal of TREC-COVID is to contribute to the response to the COVID-19 pandemic by identifying answers to many pressing questions and building infrastructure to improve search systems [8]. All five rounds of this Track challenged participants to perform a classic ad-hoc search task on the new data collection CORD-19. Our solution addressed this challenge by applying the Continuous Active Learning model (CAL) and its variations. Our results showed us to be amongst the top scoring manual runs and we remained competitive within all categories of submissions.
Frequency-compensated PINNs for Fluid-dynamic Design Problems
Venue: arXiv cs.LG 2020
Year: 2020

Incompressible fluid flow around a cylinder is one of the classical problems in fluid-dynamics with strong relevance with many real-world engineering problems, for example, design of offshore structures or design of a pin-fin heat exchanger. Thus learning a high-accuracy surrogate for this problem can demonstrate the efficacy of a novel machine learning approach. In this work, we propose a physics-informed neural network (PINN) architecture for learning the relationship between simulation output and the underlying geometry and boundary conditions. In addition to using a physics-based regularization term, the proposed approach also exploits the underlying physics to learn a set of Fourier features, i.e. frequency and phase offset parameters, and then use them for predicting flow velocity and pressure over the spatio-temporal domain. We demonstrate this approach by predicting simulation results over out of range time interval and for novel design conditions. Our results show that incorporation of Fourier features improves the generalization performance over both temporal domain and design space.
Weakly- and Semi-supervised Evidence Extraction
Venue: arXiv cs.CL 2020
Year: 2020

For many prediction tasks, stakeholders desire not only predictions but also supporting evidence that a human can use to verify its correctness. However, in practice, additional annotations marking supporting evidence may only be available for a minority of training examples (if available at all). In this paper, we propose new methods to combine few evidence annotations (strong semi-supervision) with abundant document-level labels (weak supervision) for the task of evidence extraction. Evaluating on two classification tasks that feature evidence annotations, we find that our methods outperform baselines adapted from the interpretability literature to our task. Our approach yields substantial gains with as few as hundred evidence annotations. Code and datasets to reproduce our work are available at https://github.com/danishpruthi/evidence-extraction.
Training Wake Word Detection with Synthesized Speech Data on Confusion Words
Venue: arXiv cs.LG 2020
Year: 2020

Confusing-words are commonly encountered in real-life keyword spotting applications, which causes severe degradation of performance due to complex spoken terms and various kinds of words that sound similar to the predefined keywords. To enhance the wake word detection system's robustness on such scenarios, we investigate two data augmentation setups for training end-to-end KWS systems. One is involving the synthesized data from a multi-speaker speech synthesis system, and the other augmentation is performed by adding random noise to the acoustic feature. Experimental results show that augmentations help improve the system's robustness. Moreover, by augmenting the training set with the synthetic data generated by the multi-speaker text-to-speech system, we achieve a significant improvement regarding confusing words scenario.
An Incentive Based Approach for COVID-19 using Blockchain Technology
Venue: arXiv cs.CY 2020
Year: 2020

The current situation of COVID-19 demands novel solutions to boost healthcare services and economic growth. A full-fledged solution that can help the government and people retain their normal lifestyle and improve the economy is crucial. By bringing into the picture a unique incentive-based approach, the strain of government and the people can be greatly reduced. By providing incentives for actions such as voluntary testing, isolation, etc., the government can better plan strategies for fighting the situation while people in need can benefit from the incentive offered. This idea of combining strength to battle against the virus can bring out newer possibilities that can give an upper hand in this war. As the unpredictable future develops, sharing and maintaining COVID related data of every user could be the needed trigger to kick start the economy and blockchain paves the way for this solution with decentralization and immutability of data.
A Framework for Prediction and Storage of Battery Life in IoT Devices using DNN and Blockchain
Venue: arXiv cs.CR 2020
Year: 2020

As digitization increases, the need to automate various entities becomes crucial for development. The data generated by the IoT devices need to be processed accurately and in a secure manner. The basis for the success of such a scenario requires blockchain as a means of unalterable data storage to improve the overall security and trust in the system. By providing trust in an automated system, with real-time data updates to all stakeholders, an improved form of implementation takes the stage and can help reduce the stress of adaptability to complete automated systems. This research focuses on a use case with respect to the real time Internet of Things (IoT) network which is deployed at the beach of Chicago Park District. This real time data which is collected from various sensors is then used to design a predictive model using Deep Neural Networks for estimating the battery life of IoT sensors that is deployed at the beach. This proposed model could help the government to plan for placing orders of replaceable batteries before time so that there can be an uninterrupted service. Since this data is sensitive and requires to be secured, the predicted battery life value is stored in blockchain which would be a tamper-proof record of the data.
SGB: Stochastic Gradient Bound Method for Optimizing Partition Functions
Venue: arXiv cs.LG 2020
Year: 2020

This paper addresses the problem of optimizing partition functions in a stochastic learning setting. We propose a stochastic variant of the bound majorization algorithm that relies on upper-bounding the partition function with a quadratic surrogate. The update of the proposed method, that we refer to as Stochastic Partition Function Bound (SPFB), resembles scaled stochastic gradient descent where the scaling factor relies on a second order term that is however different from the Hessian. Similarly to quasi-Newton schemes, this term is constructed using the stochastic approximation of the value of the function and its gradient. We prove sub-linear convergence rate of the proposed method and show the construction of its low-rank variant (LSPFB). Experiments on logistic regression demonstrate that the proposed schemes significantly outperform SGD. We also discuss how to use quadratic partition function bound for efficient training of deep learning models and in non-convex optimization.